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Working with SPSS

Working with SPSS

A Step-by-Step Guide For Prof PJ’s ComS 171 students

Contents Prep the Excel file for SPSS ........................................................................................................................... 2

Prep the Excel file for the online survey: .................................................................................................. 2

Make a master file .................................................................................................................................... 2

Clean the data in Excel .............................................................................................................................. 3

Set up SPSS file .............................................................................................................................................. 4

Get familiar with the SPSS Tabs & Windows ............................................................................................ 4

Copy in your Excel data into SPSS’s Data worksheet ................................................................................ 4

Set up your variables in SPSS in the Variables worksheet ........................................................................ 4

Print out the SPSS Codebook .................................................................................................................... 6

Check & Clean Your SPSS File (Run rough tables) ..................................................................................... 7

Run SPSS Tables ............................................................................................................................................ 9

Run Univariate Statistics (Frequencies and Descriptive Statistics) ........................................................... 9

Run Bivariate Statistics (Crosstabs) ......................................................................................................... 12

Run Means Comparisons (ANOVAs) ....................................................................................................... 14

Practice files are: Beginning of Semester Survey data (in Excel) and questionnaire

Working with SPSS

Prep the Excel file for SPSS

MAKE A COPY OF ALL FILES!!!! Work on the copies, not the originals. NOTE: This section is not specific to the sample files on the Beginning of the Semester Survey. It explains the basic techniques as they apply to your survey, with all four data collection methods (online, telephone, intercept self-administered, and intercept interviewer-administered).

Prep the Excel file for the online survey: • Change “words” to numbers using Replace All option.

For example: Categories for a question could be: 1 Very Low 2 Low 3 High 4 Very High These words show up instead of the numbers.

• Highlight (select) the columns you want to make the replacements in. • Find & Select > Replace > Find what =Words you want to change; Replace with = the number you

want to replace the word with. Click Replace All.

Careful: Pay attention to the order you make the replacements! If you don’t, you may end up “Very 3” if you change “High” to 3 before you change “Very High” to 4.

Make a master file This file will contain all your data (from all four data collection methods).

• At the top (the first row), make sure you have your question numbers.

• Then copy/paste your data from all files in there, keeping note of which ones came from which research method.

Careful: Pay attention to the order of the fields! You may have to insert columns or reorganize them to make sure all the fields line up properly.

Working with SPSS

Remember, you will add two variables (columns) at the end that will identify data collection method and interviewer. Put that data in now, if you haven’t already.

Data collection method Interviewer 1) Telephone 1) Martha 2) Email 2) Jose 3) Intercept Interview 3) Meng 4) Intercept Self-Administered 4) Tania 5) Dave

• Add a field for “Respondent ID” (if you haven’t already done it). For any paper surveys (e.g., the

intercept surveys or your telephone survey response sheet), make sure you have put a respondent ID on each questionnaire and that respondent ID is in your Excel Data file.

• Add a field for “Case ID” (this should be the first column). Number all of them… from 1 to 100

(or more, if you happen to have more). This way, if you need to look up data from your original survey stuff, you should be able to narrow down which ones you have to look at.

• Open-endeds/”Other” responses: If you have any of these, make sure these are typed in.

Clean the data in Excel This is very helpful… before you copy the data into SPSS. If you are good at Excel, you might be able to do some preliminary calculations to help in data cleaning.

• Check your skip patterns. If your instructions were to have a respondent skip a question based upon a previous question, did they really do it? If no, delete out or fix their responses. (You may need to do this in SPSS when you run frequency tables, but it helps if you look at it now.)

• Be consistent; develop a “protocol” for handling the data.

Consider the following:

• Determine how you will tell if a field is blank (e.g., skipped question), a Refusal to Answer (they should have answered, but didn’t), or a meaningful number like a 0. Suggestion: 99 means Refusal, blank means skipped, 0 means it was a 0 (as in “0 times eating fast food”), etc.

• If a respondent doesn’t answer a question the way you were expecting (e.g., you wanted one response, like “4” and you got a different response, like “4-7”), will you take the lower number? The middle number? Etc. Figure this out before you really get into the data; otherwise you could be biased in your decisions.

Now that you have data cleaned and prepped, we can now work in SPSS.

Working with SPSS

Set up SPSS file This includes copying the Excel data into SPSS, setting up your variables, printing out the SPSS codebook, and cleaning the data. Open the SPSS program (sometimes it takes a minute for SPSS to open up; just be patient). Choose the “Type in Data” when asked.

Get familiar with the SPSS Tabs & Windows There are three tabs or windows you want to know well:

• Data worksheet (1st “tab” at the bottom lower left). This is where you will copy all your data.

• Variables worksheet (2nd “tab” at the bottom lower left). This is where you tell SPSS what all this data means (for example, the first column carries the data for Case Number, what a “1” in column 2 mean, etc.)

• Output window. This appears when you run your tables. It will not show up when you first get into SPSS. It opens up as a separate window.

Copy in your Excel data into SPSS’s Data worksheet This is just a matter of a copy/paste command. In Excel, select (highlight) all the data you want

to bring over MINUS the first row (the one that identifies your question numbers). Then go to the first cell in SPSS and paste it. (It may take some time for SPSS to do this; it’s not instantaneous…)

Once this is done, SAVE the file. (You are likely done now with the Excel file, so you can close it.)

Set up your variables in SPSS in the Variables worksheet Click on the Variable tab.

Name—The name of your variable. I tend to use the question number. It has to start with a letter.

Suggestion: Case, Q1, Q1a, etc. Type – The variable type is most likely number or string (a “string” variable is a text response, like an

“other” or a response to an open-ended question).

Working with SPSS

Width and Decimals –This identifies how the data will show up in the Data screen. Width refers to

number of numbers allowed (for example, 1.00 would be a Width of three, as would be 126). Decimal refers to number of places in the decimal (for example, 1.00 would have a decimal of 2, 1.000 would be a Decimal of 3). I usually like to keep the Width down, but that usually means playing with the Decimal field.

HINT: Change the Decimal field first (make decimals 0 if you are like me and want whole

numbers in the file, meaning 1 instead of 1.00), then change the Width to 1 or 2. If you change the Width first, SPSS will chide you because the following field, Decimals, may make the Width inappropriate (you change the width from 8 to 1, but you are identifying 2 in Decimal, which means you need at least 3 in the Width—not a problem, but it can get annoying).

Labels—This is where you identify what the question really is. You could type the entire question

wording in, but that gets tiresome. I suggest coming up with a shorthand version.

Examples: • If the question is about Age, then type Age in the Label field. • If the question asks respondents if they would be likely to purchase a particular item, you

could type: Likelihood of Purchase • If you have a series of questions on likelihood of purchasing a variety of products, maybe

you could say: Likelihood of Purchase: Subway on one variable, then on the next one type Likelihood of Purchase: McDonalds, and so on.

NOTE: You can copy and paste this section, then just change where necessary (copy Likelihood of Purchase: Subway and, if you have 5 other fast food restaurants you are testing, highlight the next 5 variable labels and paste it. Then go change Subway to McDonalds on the McDonalds question, Subway to In-N-Out on the In-N-Out question, etc.

Values—This is where you identify the response categories for the variable. Remember that you

changed the word responses in your datafile (e.g., “Yes,” and “No”) to numbers (1 and 2)? Now you tell SPSS what those numbers mean. Essentially, you are creating a “codebook.”

Click on the box and the Value Labels pop-up window… well…

pops up. Here you enter the code number (in the Value box), the label

(make it short—you’ll appreciate that later), and click “Add,” continuing until you have all the labels in. Note that you can correct if you make an error with the Change or Remove options. When you are done, click OK.

When you get back into the Variable view, you will only see part of your value labels

(response categories). If you click on the variable, a little box with … shows up. Clicking on this will bring up the Value Labels pop-up window again.

Missing – You will only use this field if you want to exclude some values from the data calculations.

You will keep those values in the file, but you will declare them as “missing values.”

Working with SPSS

For example, you have a rating scale of 1 = Poor, 2 = Fair, 3 = Good, 4 = Excellent, 5 = Don’t know, and 6 = Refused. If you run means on this, the 5’s and the 6’s will throw off your data. So, you want to exclude those answers from your calculation. Rather than going into the file and deleting them, you declare them missing in this field.

As is the case with the values, you click on the field and the

Missing Values window pops up. By default, No missing values is selected.

In the above example, you have a choice. You can declare 5 and

6 as discrete missing values (you identify each value separately—discretely—and there are there are three fields you can input here, for a maximum of 3 discrete missing values), or you can delete a range (low of 5 and high of 6). Notice that in the Range option, you can declare an additional missing value (perhaps you want to declare 0 as a discrete missing value, plus a range from 5-6).

Columns – This refers to how the data tab will display. You can make the size of the columns 5

“characters” wide, or whatever you want. It doesn’t affect the calculations at all; it just helps you when you look at data on the Data tab.

Align – Again, this refers to how you will see the data in the Data tab. I tend to just leave it as is. Measure – this refers to whether the data should be treated as Scale, Ordinal, or

Interval. (You should already know the difference by now.) Just click on the box a drop-down menu shows up. Just select the type of data and you are done here.

Role – Just ignore this field. Make sure you save everything (and I highly recommend you make a back-up copy) before you go

onto analyze the data.

Print out the SPSS Codebook This will summarize all the work you did setting up the variables.

In either tab (the Data View or the Variable View, choose File >Display Data File Information > Working File. It will look like nothing happened. Go to the Output window and you will see two sets of printouts, one on top of the other. The first is Variable Information. It lists the variables and the associated column (position) numbers, and then the labels, level of measurement (and some other stuff we are not really going to use for this class project:

Working with SPSS

The second is the Variable Values listing. This identifies what the

“codes” for each variable are. In this example, the Variable Information table lists Q1a as “Print books.” The Variable Values listing tells you that if there is a 1 in that column, it means “Yes,” and a 2 in that column means “No.”

These two listings together comprise the SPSS codebook. It may

help you later, so print it out.

Check & Clean Your SPSS File (Run rough tables) 1. Run frequency counts/percentages for each question.

(If you have any open-ended questions, you might want to skip those in this step. a. From any tab, choose Analyze > Descriptive Statistics > Frequencies

from the top menu bar. You will get a pop-up window where you can select the variables

you want to use for your frequency analysis.

Working with SPSS

b. Click on the variables in the left box (no need to choose Case) and either drag them over or click the arrow to move them over. If you make a mistake and move something over to the Variable(s) box you didn’t want to, click on the variable in the Variable(s) box (right side) and the arrow between the two boxes will reverse, allowing you to click it and remove the variable from the Variable(s) box.

Click the OK button and you should get something that looks

like this: You can see that, in this example, 49 people responded to

each of the two variables (Q1a Print books and Q1b Newspaper).

For the table on Print books, you can see how those 49

people responded. 28 of them (or 57.1%) said “yes” and 21 of them (or 42.9%) said “no.”

2. Review the frequency tables for errors.

• Look for “outliers” (in the previous example, if you have a Yes, No, and 8 show up as the answers… you know that there were no category 8 responses to this question! This must be an error!).

If you find errors like these, you need to figure out what happened and fix them.

These types of errors may be due to the following:

• You copied/pasted/keyed data into incorrect fields. Perhaps there was a field you accidently

skipped (hit Tab too many times), throwing off the subsequent data points. • You had a data entry error (your fingers were on the wrong keys, etc.). • You forgot to add a field when you tried to match up the online, telephone, face-to-face

self-administered, and face-to-face interview administered data. • The respondent answered the question incorrectly.

Do NOT make the change on any original data files! You want to keep these files “pure,” just in case you need to start over from scratch.

• Look for obvious typos. Maybe you copied the wrong labels over. Or you forgot to type in

the labels. You can fix these right on the SPSS file.

• Check for branching errors. Say your questionnaire looked something like this:

1. Do you read ebooks? 01) Yes 02) No

Working with SPSS

2. What devices have you used for reading ebooks? a. Smartphone 1) Yes 2) No b. Tablet 1) Yes 2) No c. Kindle 1) Yes 2) No d. Computer 1) Yes 2) No e. Other 1) Yes 2) No Obviously, those respondents who said “No” on the first question(Q1) should not be answering the next set of questions (Q2a-2e), so they should “branch” or “skip around” that set of questions. So, you run a set of frequencies on Q1, Q2a, Q2b, Q2c, Q2d, and Q2e. Pay attention to the number of respondents who answered 2 (No) on Q1. If 40 respondents said 2 on Q1, and your branching is correct, then you should see the number 40 show up as System Missing on each of Q2a, Q2b, Q2c, Q2d, and Q2e. If you see 39, then one person who said No on Q1 answered the Q2 series. Time to find them!

Regardless of how the error(s) occurred,, you now need to fix the error(s). Make the change in both your copy of the Excel file and the SPSS file.

Run SPSS Tables Now it’s time to run tables:

• Univariate statistics include frequencies and means (also called “descriptive statistics”). • Bivariate statistics include crosstabulation and means comparisons (ANOVAs).

Run Univariate Statistics (Frequencies and Descriptive Statistics) Think about what kind of data you have and what type of data analysis would work for each question. • Frequency counts and percentages are appropriate for questions gathering categorical data. You

want to know what percentage of respondents said X. You may also want to run frequencies on some ordinal or scale questions.

• Descriptive Statistics (means, medians, modes) work for questions gathering ordinal and

scale/ratio level data. For example, you ask people if they’ve read any traditional print books for pleasure reading in the last month. You will want to run a frequency analysis on this question because you will want to report the percentage who read ebooks and the percentage who do not. The resulting table may show that, of the 49 respondents, 57.1% of them reported reading print books and 42.9% did not.

Working with SPSS

You could ask another question as a scale (gathering ordinal or interval level data), such as: “How would you rate your driving in comparison to others?” You can run a frequency on this because you want to know the exact percentage who gave each response. (How interesting is it to see that over half of the respondents—36.7% + 25.6% = 65.3%—considered themselves to be better than average drivers…a mathematical impossibility!) On this same driving question, you will likely want to run descriptive statistics (in particular, you probably want to know the mean). This type of analysis would show the following: 1. Running Frequency Tables:

You already know how to run a frequency table because you did it earlier when you were checking to see if you had clean data. But, to refresh your memory: Analyze > Descriptive Statistics > Frequencies (the rest will come to you).

2. Running Descriptives (Means, Medians, Modes, Standard Deviation):

In the example we are using, the following questions (the Q5 series) would be appropriate to run descriptive statistics:

5. How would you compare yourself to the average person in the following areas? Much worse Worse Average Better Much Better a. Driving 1 2 3 4 5 b. Healthy eating 1 2 3 4 5 c. Exercise habits 1 2 3 4 5 d. Frequency of washing hands 1 2 3 4 5 e. Frequency of brushing teeth 1 2 3 4 5 f. Frequency of flossing teeth 1 2 3 4 5 g. Math skills 1 2 3 4 5 h. Vocabulary skills 1 2 3 4 5 i. Awareness of current events 1 2 3 4 5 j. Not procrastinating 1 2 3 4 5 k. Paying bills on time 1 2 3 4 5 l. Managing credit card debt 1 2 3 4 5 m. Fear of speaking in public 1 2 3 4 5 n. Work ethic 1 2 3 4 5 o. Study ethic (as compared to other students) 1 2 3 4 5

This is very similar to running Frequency tables.

Working with SPSS

a. From any tab, choose Analyze > Descriptive Statistics > Descriptives from the top menu bar. Just as you did in running frequencies, you will get a pop-up window where you can select

the variables you want to use for this statistical analysis. b. Select the variables you want to run means on. In

this example, we will choose the Q5 series (assessment of skills in comparison with the “average” person).

Now, there are other things you can do in

options (like identify the order that you want the variables to display—do you want the highest mean to show up at the top of the list?), but we won’t go into that now.

Working with SPSS

Run Bivariate Statistics (Crosstabs) Now you want to know whether relationships exist between variables. For example, are males more likely to eat meals in the car than females? Remember the null and alternative hypotheses? You are essentially testing the null hypothesis that there are no differences in the answers to this question based upon a respondent’s sex. There is one main alternative hypothesis: There is a difference in frequency of eating in the car based upon a respondent’s sex. Even though there is not likely to be a cause and effect relationship between the two variables (being male is not likely to “cause” a person to eat in the car), you might want to think about the two variables this way. Is it more likely that eating in the car influences a person’s sex? Or that a person’s sex might influence their likelihood of eating in the car? Probably the latter. 1. Identify the Independent and Dependent variables. There are two questions (variables) involved in our example:

3. How often do you eat meals in the car? 01) Never 02) Seldom 03) Occasionally 04) Frequently 05) Always

and

9. What is your sex?

01) Male 02) Female

So, the frequency of eating in the car variable (Q3) is “dependent” on the respondent’s sex variable (Q9). Therefore, Q3 (Frequency of Eating in Car) would be the dependent variable and Q9 (sex) would be the independent variable.

2. Run the Crosstabulation Analysis:

a. From any tab, choose Analyze > Descriptive Statistics > Crosstabs from the top menu bar.

You will get a pop-up window asking you to identify

rows and columns: b. To make it easy on yourself, put the independent

variable (Q9-Sex) in the Row(s) box and the dependent variable (Q3-Frequency of eating in car) in the Column(s) box.

c. Now you have to select which crosstabulation

Working with SPSS

statistic(s) you want. So, click the Statistics “button” in the upper right corner.

You typically want to run chi-square to determine if any significant

differences exist. If they do, then you will want to know the strength of association (how strong is the correlation between the two variables?). For nominal variables, you should choose either Contingency coefficient and/or Phi and Cramer’s V.

You can certainly select the Chi-square statistic, but if you choose

either of the other two tests, it will automatically calculate Chi-Square.

Click Continue and you’ll be back to the original Crosstabs pop-

up window. d. One more thing: Click on the Cells “button” and make sure

Observed is checked in Counts. Then check the Rows box in Percentages. Click Continue, then (in the original Crosstabs pop-up window,

click OK. e. Read & understand the resulting table:

23.8% of males said they “never” eat in the car, versus 10.7% of females. Look at the “Frequently” column: No men said they “frequently” eat in the car, but 25.0% of females did.

Working with SPSS

Notice that the percentages in a row add across to the 100% in the total column (23.8 + 38.1 + 38.1 + .0 = 100%). That tells you that you compare between rows: While 38.1% of males said they “seldom” eat in the car, that compares to 25.0% of women. Now look at the Symmetric Measures table. The number that appears in the Approx. Sig. column is the Chi-square. For any differences that you are observing to be considered statistically significant (causing you to reject the null hypothesis), the Chi-Square needs to be less than .05 (using the 95% confidence level). This table shows a Chi-Square at .066—greater than .05, meaning that any differences we observe on the table are likely due to chance or sampling error. If the Chi-square statistic was less than .05, you would look next at the Phi, Cramer’s V, and Contingency coefficient statistics. These range from 0.00 (no correlation) to 1.00 (perfect correlation). These help you determine, IF THE CHI-SQUARE IS GREATER THAN .05, how strong the correlation is between the two variables.

Run Means Comparisons (ANOVAs) Say you want to know whether males are more likely to perceive themselves as better drivers than females. You’ve already run a mean on the overall population earlier, but you don’t know a) if there is a difference in the means based upon sex, or b) if there is a difference, if it is statistically significant. Remember the null and alternative hypothesis? You are essentially testing the null hypothesis that there are no differences in the answers to this question based upon a respondent’s sex. There are two alternative hypotheses: That males are more likely to assess their driving skills higher than females, or that females are more likely to assess their driving skills higher than males. 1. Identify the Independent and Dependent variables. There are two questions (variables) involved in our example:

5. How would you compare yourself to the average person in the following areas? Much worse Worse Average Better Much Better a. Driving 1 2 3 4 5

and

9. What is your sex?

01) Male 02) Female

Even though there is not likely to be a cause and effect relationship between the two variables (being male is not likely to “cause” a perception of being a better than average driver), you might want to think about the two variables this way. Is it more likely that the perception of driving influences a person’s sex? Or that a person’s sex might influence their perception of driving? Probably the latter.

Working with SPSS

So, the perception of driving variable is “dependent” on the respondent’s sex variable. Therefore, Q5a (Perception of Driving) would be the dependent variable and Q9 (sex) would be the independent variable.

2. Run the Mean comparisons:

a. From any tab, choose Analyze > Compare Means > Means from the top menu bar.

You will get a pop-up window where you can select the

variables you want to use for this statistical analysis.

Note: You can run multiple means comparisons at the same time, as long as you keep in mind what the Independent Variable(s) is/are. Select the dependent variable(s) and move them to (guess where?) the Dependent List box. Then select the independent variable(s) and move it/them to the (another guess?) Independent List box.

b. Now click the Options “button” in the upper right corner.

The next pop-up window you will get has three “cell statistics” already selected for you: Mean, Number of Cases, and Standard Deviation.

You need to also select “Anova table and eta” in the Statistics for First Layer box. That will help you determine if the means you see are statistically significant. Click Continue, then “OK” on the first pop-up window. You will get a series of tables: Case Processing Summary (you can probably ignore this), Report, and ANOVA Table. The Report table is the first one you look at.

Working with SPSS

It displays lots of interesting data: For the 21 males for whom we have data, the mean is 4.24 (on a 5-point scale; remember, the higher the mean, the higher the assessment of a person’s driving ability in relation to the “average” person). Standard deviation is .768. For the 28 females, the mean was 3.61 (standard deviation is .956). Overall, the mean for all 49 respondents who answered the question was 3.88 (standard deviation of .927). So we know that our survey discovered a difference in the answers on this question based upon sex, but is that difference statistically significant or could it be due to chance or sampling error? That’s where the next table comes into play: ANOVA Table. There are a lot of numbers in this table, but the one we want is in the Sig. column. In most cases we set the acceptable confidence level at 95%, meaning we are looking for a p<0.05. Compare the number in the Sig. column with 0.05. If it is lower than 0.05, then we conclude that any differences we observe are statistically significant. If it is greater than or equal to .05, then we reject the alternative hypothesis that there is a difference in the answers to this question based on sex and accept the null hypothesis (that there are no differences in answers based upon a respondent’s sex). So, what do we find? Sig. (in this example) is .017, which is less than 0.05. Therefore, our data support the alternative hypothesis that males tend to assess their driving skills higher than females do.

3. If you have at least three levels in the independent variable, run ANOVA tables independently, using Tukey as Post Hoc.

This is basically the same as you did above with the means comparison, but runs a more stringent test and works for independent variables with more than two levels. For example, there are only two levels (response categories) in the sex variable (male and female).

Working with SPSS

Just for this example, we will run assessment of driving as the dependent variable by concern with likelihood of succeeding in this class (independent variable, or “factor”). (Okay, this relationship doesn’t make sense to me, either, but I needed an example with more than two levels.) 11. How concerned are you with your likelihood of succeeding in this class. Use a 1-5 scale, 1 meaning you are NOT at all concerned and 5 meaning you are VERY concerned.

a. From any tab, choose Analyze > Compare Means > ANOVA from the top menu bar. You will get a pop-up window where you can select the variables you want to use for this

statistical analysis. Again, same routine, only instead of the bottom box being called “Independent List,” it’s called “Factor.”

Now choose Post Hoc (the middle “button” on

the right), and click Tukey in the next pop-up window. (Note that you can change the Significance level in the bottom; the default is 0.05.) Click “OK” and then you are back to the One-Way ANOVA pop-up.

Now choose Options, then check Descriptives,

then click Continue.

Now you are back to the One-Way ANOVA pop-up window. Click OK.

You get a couple of tables similar to what you got above (but with a few more statistics):

Not surprisingly, the Sig. of .368 is greater than 0.05, so we know that any differences we observe are likely due to chance or sampling error, meaning we accept the null hypothesis that there is no difference in responses to the driving question based upon concern for success in this class.

Working with SPSS

NOTE: Even though the table says that those who are very concerned about success in this class had a higher mean (4.10) than those who are not at all concerned (3.00), we have to conclude that this difference is not significant.

IF THERE WERE significant differences, you should be wondering where the statistically significant differences are? For example, is the “very concerned” mean statistically significant from the “concerned” mean? Or from the “not at all concerned” mean? That’s when you consult the next table, Post Hoc tests (using Tukey):

Check the Sig. column to see if any are less than 0.05. The first row compares the means for “Not at all concerned” on the variable of driving against all the other levels in the independent variable (shown in the second column). Surprise! Just as we suspected, no significant differences!s

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